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REVIEW 4 major objections 6 minor 20 references

Performance of the image persistence model for Euclid infrared detectors

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A fluence-dependent power-law model predicts image persistence in Euclid's NISP infrared detectors to within a few electrons, and in-flight data are on average compatible with ground calibration.

desk verdict Solid H2RG persistence data and a genuinely new above-saturation effect, but the few-electron accuracy claim is in-sample and flight agreement stays qualitative. read the letter →

arxiv 2506.02775 v1 pith:OWUDXZHA submitted 2025-06-03 astro-ph.IM

B. Kubik , R. Barbier , G. Smadja , S. Ferriol , Y. Conseil , Y. Copin , W. Gillard , S. Dusini
show 94 more authors
K. Jahnke E. Prieto N. Auricchio E. Balbi A. Balestra P. Battaglia V. Capobianco R. Chary L. Corcione F. Cogato G. Delucchi E. Franceschi L. Gabarra F. Gianotti F. Grupp E. Lentini S. Ligori E. Medinaceli G. Morgante K. Paterson E. Romelli L. Sauniere M. Schirmer C. Sirignano G. Testera M. Trifoglio A. Troja L. Valenziano M. Frailis M. Scodeggio J.-C. Barriere M. Berthe C. Bodendorf A. Caillat M. Carle R. Casas H. Cho A. Costille F. Ducret B. Garilli W. Holmes F. Hormuth A. Hornstrup M. Jhabvala R. Kohley D. Le Mignant P. B. Lilje I. Lloro C. Padilla G. Polenta J.-C. Salvignol G. Seidel B. Serra A. Secroun L. Stanco R. Toledo-Moreo S. Anselmi E. Borsato L. Caillat C. Colodro-Conde V. Conforti J. E. Davies A. Renzi F. Dal Corso S. Davini A. Derosa J. J. Diaz S. Di Domizio D. Di Ferdinando R. Farinelli A. G. Ferrari F. Fornari F. Giacomini O. Krause F. Laudisio J. Macias-Perez J. Marpaud N. Mauri R. da Silva M. Niclas F. Passalacqua I. Risso P. Lagier A. N. Sorensen P. Stassi J. Steinwagner M. Tenti C. Thizy S. Tosi R. Travaglini O. Tubio C. Valieri S. Ventura C. Vescovi J. Zoubian
This is my paper · ORCID
classification astro-ph.IM
keywords imagepersistenceHgCdTeH2RGEuclidNISPinfrareddetectorspower-lawdecaychargetrappingdetectorcharacterization
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that the afterglow left by bright sources in Euclid's 16 near-infrared detectors, an effect called image persistence, can be predicted by a power-law decay whose two parameters depend on how much light the detector just saw. For stimuli below saturation, the model claims agreement with measured persistence at the level of a few electrons, using six fitted parameters per pixel. Such a predictive model matters because persistence contaminates later exposures and, if uncorrected, degrades the photometry and redshifts that Euclid's cosmology program depends on. The paper also claims that persistence parameters measured during in-orbit calibration are on average compatible with the ground-derived values, though the match is not exact for every detector.

What carries the argument

The load-bearing object is the power-law persistence law with fluence-dependent parameters. The form $I(S,t)=\alpha(S)(\tau/(t-t_0+\tau))^{\beta(S)}$ encodes a broad distribution of trapping time constants, and the two empirical functions $\alpha(S)$ and $\beta(S)$ capture how the initial current and the decay slope respond to stimulus strength. The fitting procedure is two-stage: for each fluence level, $\alpha$ and $\beta$ are estimated from the integrated persistence current in dark ramps, and then the six constants $a_1,b_1,c_1,a_2,b_2,c_2$ are fitted pixel by pixel to describe the fluence dependence. The parameter $\tau$ prevents divergence at the end of exposure, and using per-pixel parameters lets the model absorb the nonuniform trap distributions seen in the persistence maps.

What would settle it

Take an in-flight sequence in which a bright star has crossed a NISP detector, run the six-parameter model forward from the actual fluence history, and compare the predicted persistence with the measured residual image in the next pointing; if the residual exceeds a few electrons r.m.s. for one of the detectors flagged as discrepant, the ground-to-flight transfer claim fails.

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Extended reading notes

Core claim

The central claim is that persistence current in NISP's HgCdTe H2RG detectors below saturation follows the power-law decay $$I(S,t)=\$\alpha$(S)\left(\frac{\tau}{t-t_0+\tau}\right)^{\$\beta$(S)},$$ with the fluence-dependent amplitude and index $$\$\alpha$(S)=(a_1+b_1S)\left(1-\exp(-S/c_1)\right),\qquad \$\beta$(S)=a_2\left(1+S/b_2\right)^{c_2}.$$ Fitting these six parameters per pixel in two stages --- first per-fluence $\alpha,\beta$ from the integrated current in up-the-ramp dark exposures, then their stimulus dependence --- yields a model whose predicted persistence ramps have a bias of roughly 5--10% of the persistence signal (a few electrons) and per-pixel noise below 8 e r.m.s. for all stimuli tested. The paper further reports that persistence amplitudes across the focal plane are typically about 1% of the stimulus below saturation, that a power law fits the decay better than one, two, or three exponentials, and that in-flight persistence parameters are on average compatible with ground values with the same qualitative trends.

Load-bearing premise

The few-electron accuracy in flight depends on the assumption that persistence parameters measured on the ground at 85 K with flat-field stimuli transfer to the real Euclid observation sequence; the paper reports flight parameters are only on average compatible with ground values and can differ significantly for some detectors, without a quantitative threshold.

Editorial extensions

If this is right

  • If the model is correct, Euclid's processing pipeline can predict persistence from the recent exposure history and either mask or subtract afterimages in ordinary survey frames.
  • A few-electron predictive accuracy would remove a known systematic in photometry and redshift measurements, directly protecting the weak-lensing and galaxy-clustering analyses.
  • Because $\beta$ rises with fluence, persistence decays faster after brighter stimuli, so any correction must weight past exposures by their own signal levels rather than by a single fixed decay curve.
  • The stabilization measurements imply that calibration sequences taken after the detector has been dark for a long time reach steady state only after about 30 minutes, so persistence parameters should be derived from steady-state exposures.
  • Saturating stimuli change the persistence pattern and leave a long-lived altered detector state, so the below-saturation model does not apply after saturation and saturating events need separate handling.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same two-stage fitting scheme could be applied to any H2RG-based instrument with a flat-field stimulus sequence; the functional forms are generic and not tied to Euclid's specific filters or readout scheme.
  • The paper's 'on average compatible' statement is not backed by a statistical test; a concrete per-detector acceptance threshold, defined before comparing ground and flight parameters, would turn this into a falsifiable transfer check.
  • The measured dip in detected stimulus after a long dark period is not captured by the model, suggesting a natural extension in which an additional state variable tracks how many traps are filled at the start of each exposure.
  • A direct end-to-end test would be to feed the model the actual fluence history of a real Euclid pointing sequence containing bright stars and compare predicted persistence maps against the next dark or science exposure, quantifying the useful correction power in routine operations.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper presents an empirical image-persistence model for the 16 H2RG detectors of the Euclid NISP instrument. Ground characterisation data at 85 K with flat-field stimuli between 5,000 and 95,000 e are used to fit a power-law decay current I(S,t)=alpha(S)(tau/(t-t0+tau))^beta(S), with fluence-dependent coefficients alpha(S)=(a1+b1*S)(1-exp(-S/c1)) and beta(S)=a2*(1+S/b2)^c2, giving six parameters per pixel. The model is used to predict persistence ramps in MACC mode; the bias and noise are estimated from residuals over 15 repeated sequences, and flight Performance Verification data are compared with ground results. The paper also reports persistence above saturation, a pattern inversion at saturation, and a long-term change of detector state after saturating stimuli.

Significance. The model is directly relevant for Euclid operations: persistence correction or masking is needed for weak-lensing and photometric measurements, and the paper documents per-detector and per-pixel variability for all 16 flight detectors. The main strengths are the large ground dataset, the per-pixel approach, and the explicit attempt to compare with in-flight PV data. However, as it stands the central accuracy claim is supported only by in-sample residuals, and the flight comparison is qualitative, so the paper's operational conclusion requires additional quantitative validation.

major comments (4)
  1. [§2.5, Eq. (2)] The statement that a power law fits better than an exponential or a sum of two or three exponentials is not supported by any reported fit statistics. Please provide a quantitative model comparison (e.g., chi-square per degree of freedom, AIC/BIC, or residual r.m.s. per candidate model) for a representative set of pixels and fluences; without this, the choice of Eq. (2) is not established.
  2. [§2.6] The quoted accuracy ("bias ranges from about 5% to 10%", "median noise lower than 8e r.m.s.") is computed from residuals against the same 15 sequences used to fit alpha_i and beta_i in §2.5. This is an in-sample validation. An out-of-sample test (e.g., leave-one-sequence-out, or fit on a subset of fluences and predict the remaining ones) is needed before the Summary's "accuracy of a few electrons" can be accepted.
  3. [§2.7 and §5] The flight verification is only qualitative. The text says flight-derived alpha and beta are "on average compatible" with ground values but that differences "can be significant for some detectors", and no statistical test, per-detector values, or uncertainties are given. Because flight data are integrated over 87 s and 550 s MACC modes, the short-term decay parameters are not independently constrained in flight. Please quantify the ground-to-flight comparison per detector and state explicitly what the flight data can and cannot constrain.
  4. [§2.5, Eq. (2)] The parameter tau is introduced to avoid divergence at t=t0 but its treatment is not specified: it is not stated whether tau is fixed a priori, fitted per pixel, per fluence, or per detector. If fitted, tau is degenerate with alpha and beta; if fixed, its value and justification should be stated. The same applies to the definition of t0.
minor comments (6)
  1. [Title and header] The title contains a missing space: "forEuclid" should read "for Euclid".
  2. [§2.1] The notations UTR(286) and UTR(76) are used without definition; please define the readout mode before first use.
  3. [§2.6 footnote] The MACC(ng,nf,nd) notation is defined in the footnote but used in the main text; please ensure the definition is given at first use in the text.
  4. [§2.8] The sentence "indicates the the time constants of capture" contains a duplicated article; also, "in agreement with suggestions of [19] while [11] proposes" is a run-on sentence that should be rephrased.
  5. [§5] The sentence "The signal-dependent persistence model derived in this work for stimuli below saturation that describes persistence signals with the accuracy of a few electrons" lacks a main verb and should be rephrased.
  6. [Figure 7 caption] The caption contains a typo: "bout 5000" should read "about 5000".

Circularity Check

1 steps flagged · score 6.0 of 10

Reported few-electron accuracy is an in-sample residual: §2.6 validates the model on the same 15 sequences used to fit its six per-pixel parameters, and §2.7's flight check is only qualitative.

  1. fitted input called prediction [Section 2.6, compared with Section 5 Summary; fit described in Section 2.5]
    "The accuracy (bias) of the signal-dependent model was calculated per pixel as the average (over 15 sequences for the same fluence) difference between the linear slope fitted on the model ramp in MACC(ng,nf,nd)† generated using Eqs. (2), (3) and (4) and the linear slope fitted on the data in the same MACC mode."

    The six per-pixel parameters (a1, b1, c1, a2, b2, c2) are fitted to exactly these ground 15-sequence data in Section 2.5: 'first, we fit αi and βi per fluence Si on exposures up the ramp, and then we fit the dependence of α and β on the stimulus amplitude S for all pixels independently.' The 'accuracy of a few electrons' asserted in the Summary is therefore the residual of the model against its own fitting data, not an independent prediction. The only external check, Section 2.7, is qualitative ('on average compatible', differences 'can be significant for some detectors') and cannot confirm few-electron accuracy for short-term decay because flight data are integrated over 87 or 550 s. Thus the headline accuracy claim reduces to an in-sample goodness-of-fit.

full rationale

The paper is an empirical modelling paper: the persistence model's six parameters per pixel are obtained by fitting ground characterization data in Section 2.5. The central accuracy claim in Section 5 ('describes persistence signals with the accuracy of a few electrons') is supported by Section 2.6, which measures the difference between model-generated MACC ramps and slopes fitted to the same 15 sequences at the same fluences used for the fit. That is an in-sample residual, so the reported accuracy is not an out-of-sample prediction. The flight comparison in Section 2.7 is genuinely external evidence, but it is only qualitative and explicitly notes that ground-to-flight differences 'can be significant for some detectors'; it also states that flight data, integrated over 87 or 550 s, reduce precision for short-term persistence decay. Consequently the few-electron predictive accuracy in flight is not established by independent data. No load-bearing self-citation or uniqueness-imported-from-authors pattern is present: the cited prior Euclid detector papers are instrument-characterization references, and the power-law ansatz is compared against data rather than smuggled in via citation. Because the central quantitative accuracy claim reduces to the fit residual while a partial external check exists, the circularity score is 6 rather than higher.

Assumptions & free parameters 8 free parameters · 5 assumptions · 0 invented entities

The model is entirely empirical. All predictive content comes from parsing persistence data into a two-level power-law fit with six free parameters per pixel, plus an unspecified tau. Physical interpretation leans on prior trap models (Smith et al., Long et al.) but no new entity is postulated. The main ledger concern is the large number of per-pixel free parameters and the absence of out-of-sample validation on ground data.

free parameters (8)
  • a1 (per-pixel, in alpha(S)) = Not tabulated in text
    Coefficient in Eq. (3); fitted independently for every pixel from ground characterization data at multiple fluences.
  • b1 (per-pixel, in alpha(S)) = Not tabulated
    Linear-in-S coefficient in Eq. (3); fitted per pixel.
  • c1 (per-pixel, in alpha(S)) = Not tabulated
    Saturation scale in Eq. (3); fitted per pixel.
  • a2 (per-pixel, in beta(S)) = Not tabulated
    Prefactor in beta(S), Eq. (4); fitted per pixel.
  • b2 (per-pixel, in beta(S)) = Not tabulated
    Scale parameter inside beta(S), Eq. (4); fitted per pixel.
  • c2 (per-pixel, in beta(S)) = Not tabulated
    Power index in the fluence dependence of beta(S), Eq. (4); fitted per pixel.
  • tau (in Eq. 2) = Not specified
    Time constant introduced to avoid divergence at t=t0; the text does not state whether tau is fixed or fitted, or what value is used.
  • alpha_i and beta_i per fluence (intermediate) = Not tabulated
    Each fluence level yields per-pixel alpha and beta from fits to 15 dark exposures; these are then fitted again by Eqs. (3)-(4), so the final model is a two-level fit with these as hidden parameters.
assumptions (5)
  • domain assumption Power-law decay is the correct functional form for persistence current below saturation.
    Stated in Section 2.5: 'the power-law function fits the data better than an exponential or a sum of two or three exponentials', but no fit statistics or model comparison table is provided.
  • domain assumption Persistence from successive exposures superposes linearly.
    Equations (5) and (6) in Section 3 assume the k-th measurement is the sum of persistence integrals from previous exposures.
  • domain assumption The trapping and release model of Smith et al. [17,18] is the correct physical picture.
    Used in Section 2.8 to interpret the nonlinear dependence of alpha on S and the fluence dependence of beta.
  • domain assumption Dark exposures after a flat-field contain only persistence signal, with no other time-dependent systematics.
    Underlies all persistence amplitude measurements in Sections 2.1 and 2.7.
  • domain assumption Detectors reach a steady-state of capture and release after about 30 minutes in the dark.
    Used in Section 3 to define S_inf and P_inf from the average of the last 10 measurements.

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Cite this review

Pith. "Pith review of Performance of the image persistence model for Euclid infrared detectors." pith.science (2026). https://pith.science/paper/OWUDXZHA

@misc{pith2026250602775,
  author       = {Pith},
  title        = {Pith review of: Performance of the image persistence model for Euclid infrared detectors},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OWUDXZHA}},
  note         = {Machine review of arXiv:2506.02775}
}
read the original abstract

Large-format infrared detectors are at the heart of major ground and space-based astronomical instruments, and the HgCdTe HxRG is the most widely used. The Near Infrared Spectrometer and Photometer (NISP) of the ESA's Euclid mission launched in July 2023 hosts 16 H2RG detectors in the focal plane. Their performance relies heavily on the effect of image persistence, which results in residual images that can remain in the detector for a long time contaminating any subsequent observations. Deriving a precise model of image persistence is challenging due to the sensitivity of this effect to observation history going back hours or even days. Nevertheless, persistence removal is a critical part of image processing because it limits the accuracy of the derived cosmological parameters. We will present the empirical model of image persistence derived from ground characterization data, adapted to the Euclid observation sequence and compared with the data obtained during the in-orbit calibrations of the satellite.

Figures

Figures reproduced from arXiv: 2506.02775 by the authors.

Figure 1
Figure 1. Characteristic persistence values per detector after stimuli values of 5000 (blue) and 95 000e (red). The [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Increase in persistence amplitude RP per detector (green boxes) compared to the increase in stimuli RS (grey boxes). The description of boxes is the same as in [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Persistence structures across detectors after a flat-field below saturation (left panel) and above satu [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Median parameters α (left panel) and β (right panel) of the persistence model for one detector as function of fluence. The unfilled circles show the parameters αgd and βgd measured during ground characteriza￾tions for unsaturating stimuli. The * markers show the parame…
Figure 5
Figure 5. Figure 5: Distribution of persistence model parameters [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Difference between the α and β measured per fluence and the model prediction using Eqs. (3) and (4) for one detector. The model noise, measured as per pixel standard deviation over 15 sequences of residuals at each fluence level, is shown in the right column of [PITH_…
Figure 7
Figure 7. Figure 7: Spatial distribution of the bias (left column) and of the noise (right column) of the power-law model [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Stabilisation of detector median stimulus amplitude (left panel) and persistence amplitude (right panel) [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]
Figure 9
Figure 9. Figure 9: Relative persistence in NISP detectors for typical photometric exposure of 87s. Unsaturating stimuli [PITH_FULL_IMAGE:figures/full_fig_p012_9.png]
Figure 10
Figure 10. Figure 10: Dependence of ∆S = S∞ − S0 (left panel) and ∆P = P∞ − P0 (right panel) on the persistence amplitude P∞. The circular markers represent measurements taken before the saturating stimulus was delivered. The unfilled triangular markers represent measurements taken after t…

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